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Prevalent Crimes in the United States Research Paper

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Introduction

Crime is prevalent in the United States. Americans report incidents of crime on a daily basis. To help combat crime, it is imperative for law enforcement bodies to understand characteristics of a crime incident. For example, if there is one victim, what is the likelihood that there was one offender? Of the incidents reported, how many male and/or female victims are involved? What is the most prevalent crime? Additionally, it is crucial to understand locations that are susceptible to crime. Local, state, and federal law enforcers need this kind of information to assign resources effectively and to help seize offenders. In this regard, I will orient this research towards indentifying and analyzing such factors. Lastly, the paper will develop research examining the number of prisoners in the USA jails because of these crimes, recidivism, and the importance of education in tackling crime.

Background

Crime in the United States has been on a decline since its peak in 1980s. However, the characteristics of offenses have taken a new turn whereby offenders use sophisticated methods such as extortion, kidnapping among others (Aucoin, 2011). Normally, these crimes are reported to law enforcement during or after the incident. A new school of thought suggests that the justice system perpetrates the problem instead of solving it. Consequently, over a third of minorities lack college education because when they are supposed to be obtaining an education they are sent to prison. Once they are outside, the time for them to attend college is gone. Hence, the justice system needs to be overhauled to ensure that it does not punish. Rather, it should find a way in which the perpetrator pays for their social misdeeds without denying them a life, which makes it worse because these are minority groups. Additionally, this does not frequently happen with white males because majorities are incarcerated at between 35 and 45. At this age, a person has completed college and started a family. Hence, there will be little sentiment when the person gets out of prison.

According to the government’s statistics (in the United States for example) approximately 1 million prisoners are released annually. Most of them offend again and return to prison together with the first time offenders. The United States government spends over $30 billion to construct prison facilities. If the government were to reduce this recidivism rates by half, it would save so much. Education provides individuals with confidence in life and an alternative from crime. Once educated, a prisoner can rely solely on oneself. A prisoner is able to raise a family when they have equal chances for the available opportunities (Kleck, 2004).

However, this is not possible in a case the opportunities are skewed. Once an individual raises their family well, chances of a generational circus of crime are grossly reduced. In the long term, this is beneficial to both the society and the economy. Education in prisons provides better ways of utilizing the free time that inmates have in prison. This free time may be used for planning other evil deeds and making life for other prisoners and superintendants hard. Provisions of education bring some order as prisoners are expected to be at particular centers at particular times (Borghans, 2005).

A study of close to 20 empirical studies suggests that higher education reduces the possibility for re-incarceration of prisoners from both genders. Without any education on average 80% of the prisoners who are released from prison, return there within five years. If they were to be educated, the rate of recidivism would reduce according to the level of education achieved. The higher the education level, the lower the chances of returning to prison. For prisoners who attain a bachelor’s degree around 6% are re-incarcerated, for those who attain an AA degree around 14% are re-incarcerated, for those who attain a Masters degree, there is a zero chance of re-incarceration. It is also crucial to note that while in prison, these convicts are always in a constant torment and a dangerous environment. This may be transferred to society.

Data and methods

In order to answer the question of characteristics of a crime incident, I will use GSS data from the 1978 to 2011. I will use seven variables. Two of the variables will be categorical variable. They are most serious incident offense (msioff) and Incident location (inc_loc). The other five are quantitative variables. They include count of victims in incident (vic_count), count of offenders in incident (off_cnt), count of victims under age 18 (vlt18), count of male victims in incident (vmale), and count of female victims in incident (vfemale). The respondents to these questions were the different police and incident reporting stations in the USA. Hence, it constitutes official data of actual events. I will use three variables to determine the relationships, for example, to assess the relationship between the count of offenders in incident (off_cnt) and count of victims in incident (vic_count). I will run the data through Regression Analysis to determine the relationships. Another relationship is Incident location (inc_loc) and count of offenders in incident (off_cnt) and/or count of victims in incident (vic_count). The relationship may be crucial in predicting the number of victims in a hostage crisis when the offenders are known. I will also conduct cross tabulations to establish relationships between different variables. Lastly, descriptive statistics will be crucial in determining different measures such as the mean and standard deviation (National Opinion Research Center, 2013).

Results

Descriptive statistics

Table 1 lists statistics for the various variables such as count of offenders in incident (off_cnt) and count of victims in incident (vic_count). Table 2 shows the frequency of occurrence of various serious offenses in percentages. As Table 2 shows, robbery is the most prevalent offense recorded 98% of the time. Figure 1 also shows these frequencies in a diagrammatic format (i.e. a bar graph). Table 3 shows how often an offense happens in various locations. Most crimes (28.6%) happen in highways while the least crimes happen in churches.

Table 1: Descriptive statistics

Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
Statistic Statistic Statistic Statistic Std. Error Statistic
Count of victims in incident 22336 1.00 19.00 1.3352 .00494 .73784
Count of offenders in incident 22336 1.00 15.00 1.4436 .00568 .84852
Count of victims under age 18 19367 .00 6.00 .1556 .00323 .44973
Count of male victims in incident 19367 .00 10.00 .7723 .00463 .64406
Count of female victims in incident 19367 .00 8.00 .4029 .00428 .59592
Valid N (listwise) 19367

Table 2: Most serious offense frequency table

Most serious incident offense
Frequency Percent Valid Percent Cumulative Percent
Valid Murder and Non-negligent Manslaughter 60 .3 .3 .3
Forcible Rape 99 .4 .4 .7
Robbery 22176 99.3 99.3 100.0
Rape of a Male 1 .0 .0 100.0
Total 22336 100.0 100.0
Most serious incident offense frequencies
Figure 1: Most serious incident offense frequencies

Table 3: Frequency of incident per location

Incident location
Frequency Percent Valid Percent Cumulative Percent
Valid Terminal 39 .2 .2 .2
Bank 655 2.9 2.9 3.1
Bar 280 1.3 1.3 4.4
Church 22 .1 .1 4.5
Commercial/Office 811 3.6 3.6 8.1
Constr. Site 12 .1 .1 8.1
Conv. Store 2040 9.1 9.1 17.3
Dpt. Store 434 1.9 1.9 19.2
Drug Store 191 .9 .9 20.1
Field/Woods 228 1.0 1.0 21.1
Government/Public 62 .3 .3 21.4
Grocery 717 3.2 3.2 24.6
Highway 6386 28.6 28.6 53.2
Hotel 615 2.8 2.8 55.9
Jail 15 .1 .1 56.0
Waterway 20 .1 .1 56.1
Liquor St. 130 .6 .6 56.7
Parking 2233 10.0 10.0 66.7
Stor Fac 4 .0 .0 66.7
Residence 3489 15.6 15.6 82.3
Restaurant 1008 4.5 4.5 86.8
School 178 .8 .8 87.6
Gas Station 818 3.7 3.7 91.3
Specialty St. 550 2.5 2.5 93.7
Other/Unk 1399 6.3 6.3 100.0
Total 22336 100.0 100.0

Cross tabulations and chi-squared tests of independence

Tables 4, 6, 8, 10, and 12 indicate that there is a relationship between every two variables been cross tabulated. The inherent chi square tests indicate very little values (Incident location * Count of victims in incident Cross tabulation in Table 4 indicates that most incidents have one victim in different locations. In fact, incidents with more than five victims are limited in any location. Table 6 that cross tabulates Count of victims in incident and Count of offenders in incident indicates that most of the single victims encounter one offender. That is, most offenders target one victim in many occasions (12350). The small value of significance of P indicates a strong relationship between the variables.

Table 4: Incident location and count of victims in incident cross tabulation

Incident location * Count of victims in incident Cross tabulation
Count
Count of victims in incident Total
1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00 11.00 13.00 15.00 19.00
Incident location Terminal 38 1 0 0 0 0 0 0 0 0 0 0 0 0 39
Bank 445 163 24 10 4 2 1 3 0 2 0 1 0 0 655
Bar 223 41 8 3 0 2 0 1 1 0 0 0 0 1 280
Church 17 4 1 0 0 0 0 0 0 0 0 0 0 0 22
Commercial/Office 485 251 49 17 5 0 1 2 0 0 1 0 0 0 811
Constr. Site 8 3 1 0 0 0 0 0 0 0 0 0 0 0 12
Conv. Store 1078 860 76 22 4 0 0 0 0 0 0 0 0 0 2040
Dpt. Store 251 138 36 5 3 0 1 0 0 0 0 0 0 0 434
Drug Store 139 36 13 3 0 0 0 0 0 0 0 0 0 0 191
Field/Woods 199 20 6 3 0 0 0 0 0 0 0 0 0 0 228
Government/Public 49 8 3 1 0 1 0 0 0 0 0 0 0 0 62
Grocery 458 194 47 11 2 3 1 1 0 0 0 0 0 0 717
Highway 5652 609 88 26 10 1 0 0 0 0 0 0 0 0 6386
Hotel 388 178 36 5 1 2 5 0 0 0 0 0 0 0 615
Jail 13 2 0 0 0 0 0 0 0 0 0 0 0 0 15
Waterway 17 1 2 0 0 0 0 0 0 0 0 0 0 0 20
Liquor St. 69 47 10 2 1 1 0 0 0 0 0 0 0 0 130
Parking 1889 280 47 9 6 1 0 1 0 0 0 0 0 0 2233
Stor Fac 2 1 1 0 0 0 0 0 0 0 0 0 0 0 4
Residence 2718 519 144 65 21 13 4 1 2 2 0 0 0 0 3489
Restaurant 549 314 74 30 21 15 1 2 1 0 0 0 1 0 1008
School 152 19 5 0 1 1 0 0 0 0 0 0 0 0 178
Gas Station 561 217 33 5 1 1 0 0 0 0 0 0 0 0 818
Specialty St. 296 203 35 11 3 1 0 1 0 0 0 0 0 0 550
Other/Unk 1123 217 34 17 2 6 0 0 0 0 0 0 0 0 1399
Total 16819 4326 773 245 85 50 14 12 4 4 1 1 1 1 22336

Table 5: Chi-Square Tests for Table 4

Chi-Square Tests
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 2784.714a 312 .000
Likelihood Ratio 2423.551 312 .000
Linear-by-Linear Association 15.225 1 .000
N of Valid Cases 22336
a. 270 cells (77.1%) have expected count less than 5. The minimum expected count is.00.

Table 6: Count of victims in incident and count of offenders in incident cross tabulation

Count of victims in incident * Count of offenders in incident Cross tabulation
Count of offenders in incident Total
1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00 12.00 15.00
Count of Count victims in incident 1.00 12350 2940 1080 313 74 38 17 2 2 2 1 0 16819
2.00 2865 959 328 109 41 17 5 1 0 0 0 1 4326
3.00 434 205 91 26 12 3 0 2 0 0 0 0 773
4.00 113 59 36 22 10 3 1 1 0 0 0 0 245
5.00 34 27 9 8 4 1 1 0 1 0 0 0 85
6.00 23 15 8 2 1 0 0 1 0 0 0 0 50
7.00 11 2 1 0 0 0 0 0 0 0 0 0 14
8.00 7 3 1 1 0 0 0 0 0 0 0 0 12
9.00 2 2 0 0 0 0 0 0 0 0 0 0 4
10.00 2 2 0 0 0 0 0 0 0 0 0 0 4
11.00 0 1 0 0 0 0 0 0 0 0 0 0 1
13.00 1 0 0 0 0 0 0 0 0 0 0 0 1
15.00 0 1 0 0 0 0 0 0 0 0 0 0 1
19.00 0 0 0 0 1 0 0 0 0 0 0 0 1
Total 15842 4216 1554 481 143 62 24 7 3 2 1 1 22336

Table 7: Chi-Square Tests for Table 6

Chi-Square Tests
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 797.350a 143 .000
Likelihood Ratio 406.411 143 .000
Linear-by-Linear Association 316.853 1 .000
N of Valid Cases 22336
a. 139 cells (82.7%) have expected count less than 5. The minimum expected count is.00.

Table 8: Count of offenders in incident and count of victims under age 18 cross tabulation

Count of offenders in incident * Count of victims under age 18 Cross tabulation
Count
Count of victims under age 18 Total
.00 1.00 2.00 3.00 4.00 5.00 6.00
Count of offenders in incident 1.00 11819 1402 132 25 5 7 1 13391
2.00 3286 445 76 13 4 2 1 3827
3.00 1207 202 38 16 1 1 0 1465
4.00 361 73 14 2 2 1 0 453
5.00 103 22 8 1 2 0 0 136
6.00 43 11 3 1 1 0 0 59
7.00 16 3 1 1 1 0 0 22
8.00 5 1 1 0 0 0 0 7
9.00 3 0 0 0 0 0 0 3
10.00 1 1 0 0 0 0 0 2
12.00 1 0 0 0 0 0 0 1
15.00 1 0 0 0 0 0 0 1
Total 16846 2160 273 59 16 11 2 19367

Table 9: Chi-Square Tests for Table 8

Chi-Square Tests
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 304.961a 66 .000
Likelihood Ratio 169.325 66 .000
Linear-by-Linear Association 155.690 1 .000
N of Valid Cases 19367
a. 62 cells (73.8%) have expected count less than 5. The minimum expected count is.00.

Table 10: Count of victims in incident and count of male victims in incident Cross tabulation

Count of victims in incident * Count of male victims in incident Cross tabulation
Count
Count of male victims in incident Total
.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00
Count of victims in incident 1.00 4162 9873 0 0 0 0 0 0 0 0 0 14035
2.00 1652 1884 617 0 0 0 0 0 0 0 0 4153
3.00 179 292 179 115 0 0 0 0 0 0 0 765
4.00 35 63 64 49 32 0 0 0 0 0 0 243
5.00 6 16 19 17 18 8 0 0 0 0 0 84
6.00 1 9 11 9 9 7 4 0 0 0 0 50
7.00 0 6 2 1 2 2 1 0 0 0 0 14
8.00 1 1 5 1 0 2 1 1 0 0 0 12
9.00 0 0 0 0 0 2 1 0 1 0 0 4
10.00 0 0 1 0 1 2 0 0 0 0 0 4
11.00 0 0 0 1 0 0 0 0 0 0 0 1
15.00 0 0 0 0 0 0 0 0 0 1 0 1
19.00 0 0 0 0 0 0 0 0 0 0 1 1
Total 6036 12144 898 193 62 23 7 1 1 1 1 19367

Table 11: Chi-Square Tests for Table 10

Chi-Square Tests
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 60365.051a 120 .000
Likelihood Ratio 5102.734 120 .000
Linear-by-Linear Association 2814.345 1 .000
N of Valid Cases 19367
a. 118 cells (82.5%) have expected count less than 5. The minimum expected count is.00.

Table 12: Count of victims in incident and count of female victims in incident cross tabulation

Count of victims in incident * Count of female victims in incident Cross tabulation
Count
Count of female victims in incident Total
.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00
Count of victims in incident 1.00 9895 4140 0 0 0 0 0 0 0 14035
2.00 2048 1934 171 0 0 0 0 0 0 4153
3.00 296 282 163 24 0 0 0 0 0 765
4.00 65 74 63 35 6 0 0 0 0 243
5.00 25 18 17 17 5 2 0 0 0 84
6.00 8 12 13 10 6 1 0 0 0 50
7.00 0 2 2 2 2 6 0 0 0 14
8.00 1 1 2 1 1 3 2 1 0 12
9.00 0 1 1 1 1 0 0 0 0 4
10.00 0 0 0 0 0 3 0 1 0 4
11.00 0 0 0 0 0 0 0 1 0 1
15.00 0 0 0 0 0 1 0 0 0 1
19.00 0 0 0 0 0 0 0 0 1 1
Total 12338 6464 432 90 21 16 2 3 1 19367

Table 13: Chi-Square Tests for Table 12

Chi-Square Tests
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 46533.083a 96 .000
Likelihood Ratio 3190.226 96 .000
Linear-by-Linear Association 3693.262 1 .000
N of Valid Cases 19367
a. 95 cells (81.2%) have expected count less than 5. The minimum expected count is.00.

Table 14: Count of offenders in incident and Count of female victims in incident cross tabulation

Count of offenders in incident * Count of female victims in incident Cross tabulation
Count
Count of female victims in incident Total
.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00
Count of offenders in incident 1.00 8096 4982 240 47 14 10 0 2 0 13391
2.00 2657 1010 127 19 6 6 1 1 0 3827
3.00 1079 332 38 15 1 0 0 0 0 1465
4.00 328 104 14 6 0 0 1 0 0 453
5.00 102 23 7 3 0 0 0 0 1 136
6.00 48 7 4 0 0 0 0 0 0 59
7.00 18 3 1 0 0 0 0 0 0 22
8.00 5 1 1 0 0 0 0 0 0 7
9.00 2 1 0 0 0 0 0 0 0 3
10.00 1 1 0 0 0 0 0 0 0 2
12.00 1 0 0 0 0 0 0 0 0 1
15.00 1 0 0 0 0 0 0 0 0 1
Total 12338 6464 432 90 21 16 2 3 1 19367

Table 15: Chi-Square Tests for Table 14

Chi-Square Tests
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 530.944a 88 .000
Likelihood Ratio 386.675 88 .000
Linear-by-Linear Association 63.560 1 .000
N of Valid Cases 19367
a. 85 cells (78.7%) have expected count less than 5. The minimum expected count is.00.

Linear regression results

Tables 16 to 24 examine relationships between different variables with the aim of establishing whether one variable influences the other. However, as the model summaries indicate, the R Square and adjusted R Square values are too small as to indicate any relationships. For example, Table 16 indicates that the count of offenders and count of victims have a small relationship. Only 20% of the model is accounted for by count of offenders. The same case applies to the other model summaries. Hence, it is safe to conclude that the variables in this model do not influence each other as to causality.

Table 16: Regression of Count of female victims in incident and Count of offenders in incident

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .457a .209 .209 .68687
a. Predictors: (Constant), Count of female victims in incident, Count of offenders in incident

Table 17: ANOVA for Table 16

ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 2416.813 2 1208.406 2561.345 .000b
Residual 9135.662 19364 .472
Total 11552.475 19366
a. Dependent Variable: Count of victims in incident
b. Predictors: (Constant), Count of female victims in incident, Count of offenders in incident

Table 18: coefficients for table 16

Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) .966 .010 92.942 .000
Count of offenders in incident .120 .006 .136 21.281 .000
Count of female victims in incident .576 .008 .445 69.443 .000
a. Dependent Variable: Count of victims in incident

Table 19: Model summary Count of offenders in incident

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .119a .014 .014 .73260
a. Predictors: (Constant), Count of offenders in incident

Table 20: ANOVA for Table 19

ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 172.498 1 172.498 321.399 .000b
Residual 11986.869 22334 .537
Total 12159.367 22335
a. Dependent Variable: Count of victims in incident
b. Predictors: (Constant), Count of offenders in incident

Table 21: Coefficients for table 19

Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 1.186 .010 122.564 .000
Count of offenders in incident .104 .006 .119 17.928 .000
a. Dependent Variable: Count of victims in incident

Table 22: Model summary

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .042a .002 .002 .84778
a. Predictors: (Constant), Most serious incident offense

Table 23: ANOVA for Table 22

ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 29.030 1 29.030 40.391 .000b
Residual 16052.004 22334 .719
Total 16081.035 22335
a. Dependent Variable: Count of offenders in incident
b. Predictors: (Constant), Most serious incident offense

Table 24: coefficients for Table 22

Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 3.929 .391 10.046 .000
Most serious incident offense -.021 .003 -.042 -6.355 .000
a. Dependent Variable: Count of offenders in incident

Discussion

Returning to the original research question, it follows that cross tabulation and chi-squared tests are the most relevant analytical tools for this data. Additionally, descriptive statistics shows that majority of the offenses occur because of robbery. Hence, federal and state governments should dedicate their resources towards combating robbery. A continued incarceration of social and criminal offenders has not deterred crime. With the world population increasing by day, more and more people are finding themselves in prisons. Most of them are first time offenders and a considerably large number of recidivists. Because of this trend, it is logical to employ the use of education, especially higher education, as the social and fiscal alternative in tackling this menace.

There are far reaching positive effects of education on youths and ex prisoners. These effects have wide social and fiscal benefits. Education in itself is an avenue to employment after prison. With private and public partnership, this can bear fruits in the overall societal life. It reduces chances of an offender going back to crime and makes offenders responsible for their families. Further, education increases self-esteem, self-confidence, enables youths and ex prisoners to become role models, and most critically, increases their options in the larger society (Biraimah, 2005).

Mnemonics list

GSS Variable Variable Name
inc_loc Incident location
vic_cnt Count of victims in incident
off_cnt Count of offenders in incident
vlt18 Count of victims under age 18
msioff Most serious incident offense
vmale Count of male victims in incident
vfemale Count of female victims in incident

Reference List

Aucoin, Robert. “Information and Communication Technologies in International Education: A USA Policy Analysis.” International Journal of Education Policy and Leadership 6, no. 4 (2011): 1-11.

Biraimah, Karen. “Achieving Equitable Outcomes or Reinforcing Societal Inequalities? A Critical Analysis of UNESCO Education for All and the United States No Child Left Behind Programs.” Educational Practice and Theory 27, no. 2 (2005): 25-34.

Borghans, Heijke. “The Production and Use of Human Capital: Introduction.” Education Economics 13, no. 2 (2005): 130-133.

Kleck, Gary. “Measures of Gun Ownership Levels of Macro-Level Crime and Violence Research.” Journal of Research in Crime and Delinquency 41, no. 1 (2004): 3-36.

National Opinion Research Center, University of Chicago, General Social Surveys, 1972-2011: Cumulative Codebook. Chicago: National Opinion Research Center, 2013.

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